Differential phospho/protein analysis - CRC Batch 1
Sophie Herbst
Analysis date: 2023-10-18
Depends on
DIPG_FirstBatch_DataProcessing Script
load("../Data/Cache/Xenografts_Batch1_2_DataProcessing.RData")TODO
- Do differential abudance analysis for prep batch and mass spec run
Setup
Load libraries and functions
PTMsigDB
PtmSigdb <- readxl::read_excel("../../../General/Code/Data/db_ptm.sig.db.all.v2.0.0/data_PTMsigDB_all_sites_v2.0.0.xlsx")## Warning: Expecting numeric in D66110 / R66110C4: got '2407414-sm;d'## Warning: Expecting numeric in D66117 / R66117C4: got '3755605-sm;d'## Warning: Expecting numeric in D66118 / R66118C4: got '3829801-sm;d'## Warning: Expecting numeric in D66251 / R66251C4: got '2134717-sm;u'## Warning: Expecting numeric in D66252 / R66252C4: got '2134718-sm;u'## Warning: Expecting numeric in D66508 / R66508C4: got '1668802-me;u'## Warning: Expecting numeric in D66525 / R66525C4: got '1668801-me;u'## Warning: Expecting numeric in D66592 / R66592C4: got '2295400-sm;u'## Warning: Expecting numeric in D66650 / R66650C4: got '60885600-pa;d'## Warning: Expecting numeric in D66651 / R66651C4: got '60885601-pa;d'## Warning: Expecting numeric in D66652 / R66652C4: got '8545503-sm;u'## Warning: Expecting numeric in D66653 / R66653C4: got '8545504-sm;u'## Warning: Expecting numeric in D66654 / R66654C4: got '8545505-sm;u'## Warning: Expecting numeric in D66748 / R66748C4: got '31089521-sm;d'## Warning: Expecting numeric in D66819 / R66819C4: got '1668802-me;d'## Warning: Expecting numeric in D67030 / R67030C4: got '50455000-sm;u'## Warning: Expecting numeric in D67031 / R67031C4: got '50455001-sm;u'## Warning: Expecting numeric in D67032 / R67032C4: got '50455002-sm;u'## Warning: Expecting numeric in D67043 / R67043C4: got '31094520-me;u'## Warning: Expecting numeric in D67056 / R67056C4: got '27581511-ne;d'## Warning: Expecting numeric in D67057 / R67057C4: got '27581513-ne;d'## Warning: Expecting numeric in D67080 / R67080C4: got '2535901-sm;d'## Warning: Expecting numeric in D67086 / R67086C4: got '25092200-sm;d'## Warning: Expecting numeric in D67087 / R67087C4: got '25092201-sm;d'## Warning: Expecting numeric in D67107 / R67107C4: got '8545502-sm;d'## Warning: Expecting numeric in D67128 / R67128C4: got '2586358-me;u'## Warning: Expecting numeric in D67129 / R67129C4: got '2586359-me;u'## Warning: Expecting numeric in D67130 / R67130C4: got '2586360-me;u'## Warning: Expecting numeric in D67505 / R67505C4: got '12723517-sm;u'## Warning: Expecting numeric in D67506 / R67506C4: got '12723518-sm;u'## Warning: Expecting numeric in D67511 / R67511C4: got '27850501-sm;d'Analysis
DEP
Tyrosine
E vs ctrl
data_diff_E_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_E_vs_ctrl_pY <- add_rejections_SH(data_diff_E_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_E_vs_ctrl_pY, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pY") ## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.Return_DEP_Hits_Plots(data = pY_Set2_form, dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns## Loading required namespace: reactome.db## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## character(0)## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
PTM-SEA
GSEA_E_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)GSEA_E_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))## # A tibble: 0 × 8
## # ℹ 8 variables: pathway <chr>, pval <dbl>, padj <dbl>, log2err <dbl>,
## # ES <dbl>, NES <dbl>, size <int>, leadingEdge <list>GSEA_E_vs_ctrl_PTM %>% as_tibble() %>% arrange(desc(NES))## # A tibble: 303 × 8
## pathway pval padj log2err ES NES size leadingEdge
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list>
## 1 KINASE-iKiP_EPHA5 0.00397 0.816 0.407 0.988 1.47 2 <chr [2]>
## 2 PATH-NP_AGE_RAGE_PATHWAY 0.103 0.816 0.178 0.745 1.43 6 <chr [4]>
## 3 KINASE-iKiP_LYNB.LYN 0.153 0.816 0.135 0.615 1.33 10 <chr [4]>
## 4 KINASE-PSP_MKK6/MAP2K6 0.111 0.816 0.180 0.833 1.33 3 <chr [3]>
## 5 KINASE-PSP_EphA2/EPHA2 0.153 0.816 0.147 0.756 1.31 4 <chr [4]>
## 6 PERT-PSP_H2O2 0.138 0.816 0.160 0.818 1.31 3 <chr [3]>
## 7 KINASE-PSP_Brk/PTK6 0.0878 0.816 0.209 0.955 1.29 1 <chr [1]>
## 8 PATH-NP_IL3_PATHWAY 0.0878 0.816 0.209 0.955 1.29 1 <chr [1]>
## 9 KINASE-iKiP_EPHA3 0.162 0.816 0.146 0.801 1.28 3 <chr [2]>
## 10 PERT-PSP_GDNF 0.226 0.816 0.115 0.663 1.27 6 <chr [6]>
## # ℹ 293 more rowsRun_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 66 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 NEDD9 TGHGYVyEYPSR Y166-p 3.75
## 2 CTNND1 HYEDGYPGGSDNyGSLSR Y228-p 1.68
## 3 GAB1 DASSQDCyDIPR Y406-p 1.54
## 4 PXN VGEEEHVySFPNK Y118-p 1.29
## 5 EPHA2 QSPEDVyFSK Y575-p 1.29
## 6 EPHA2 VLEDDPEATyTTSGGK Y772-p 1.20
## 7 EPHA2 VLEDDPEATyTTSGGKIPIR Y772-p 1.20
## 8 PKP4 STTNyVDFYSTK Y1168-p 0.848
## 9 PTPN11 VyENVGLMQQQK Y580-p 0.799
## 10 PKP3 GQyHTLQAGFSSR Y84-p 0.793
## # ℹ 56 more rowsRun_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 17 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 PRKCD TGVAGEDMQDNSGTyGK Y334-p 1.35
## 2 RET DVYEEDSyVK Y905-p 0.730
## 3 RET DVYEEDSyVKR Y905-p 0.730
## 4 CAV1 YVDSEGHLyTVPIR Y14-p 0.539
## 5 PRKCD RSDSASSEPVGIyQGFEK Y313-p 0.433
## 6 PRKCD RSDsASSEPVGIyQGFEK Y313-p 0.433
## 7 PRKCD RSDSASSEPVGIyQGFEKK Y313-p 0.433
## 8 PRKCD SDSASSEPVGIyQGFEK Y313-p 0.433
## 9 PTK2 YMEDSTyYK Y576-p 0.329
## 10 STAT1 GTGyIKTELISVSEVHPSR Y701-p 0.298
## 11 PTPRA VVQEYIDAFSDyANFK Y798-p 0.114
## 12 GJA1 QASEQNWANySAEQNR Y313-p 0.00102
## 13 ARHGAP35 NEEENIySVPHDSTQGK Y1105-p -0.216
## 14 CTNNB1 NEGVATyAAAVLFR Y654-p -0.323
## 15 PFN1 CyEMASHLR Y129-p -0.332
## 16 MPZL1 SESVVyADIR Y263-p -0.450
## 17 SHC1 ELFDDPSyVNVQNLDK Y427-p -0.642Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 5 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 EPHA2 QSPEDVyFSK Y575-p 1.29
## 2 EPHA2 VLEDDPEATyTTSGGK Y772-p 1.20
## 3 EPHA2 VLEDDPEATyTTSGGKIPIR Y772-p 1.20
## 4 EPHA2 TYVDPHTyEDPNQAVLK Y594-p 0.383
## 5 CLDN4 SAAASNyV Y208-p 0.311EC vs ctrl
data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pY") Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## [1] "Regulation of PTEN gene transcription"
## [2] "Innate Immune System"
## [3] "Interferon Signaling"
## [4] "RHOG GTPase cycle"
## [5] "DAP12 interactions"
## [6] "Tie2 Signaling"
## [7] "Fcgamma receptor (FCGR) dependent phagocytosis"
## [8] "FCERI mediated MAPK activation"Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")PTM-SEA
GSEA_EC_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)GSEA_EC_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))## # A tibble: 0 × 8
## # ℹ 8 variables: pathway <chr>, pval <dbl>, padj <dbl>, log2err <dbl>,
## # ES <dbl>, NES <dbl>, size <int>, leadingEdge <list>GSEA_E_vs_ctrl_PTM %>% as_tibble() %>% arrange(desc(NES)) %>% filter(size > 9)## # A tibble: 7 × 8
## pathway pval padj log2err ES NES size leadingEdge
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list>
## 1 KINASE-iKiP_LYNB.LYN 0.153 0.816 0.135 0.615 1.33 10 <chr [4]>
## 2 PATH-NP_EGFR1_PATHWAY 0.142 0.816 0.122 0.424 1.25 57 <chr [20]>
## 3 KINASE-PSP_Ret/RET 0.280 0.816 0.0920 0.502 1.18 14 <chr [9]>
## 4 PERT-PSP_EGF 0.491 0.816 0.0649 0.456 1.01 11 <chr [4]>
## 5 KINASE-PSP_Src/SRC 0.641 0.818 0.0510 0.378 0.871 13 <chr [6]>
## 6 PATH-NP_TSLP_PATHWAY 0.853 0.936 0.0394 0.309 0.670 10 <chr [5]>
## 7 PATH-NP_PROLACTIN_PATHWAY 0.246 0.816 0.186 -0.346 -1.15 25 <chr [9]>Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 66 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 NEDD9 TGHGYVyEYPSR Y166-p 2.54
## 2 PKP3 GQyHTLQAGFSSR Y84-p 0.843
## 3 PXN VGEEEHVySFPNK Y118-p 0.741
## 4 GAB1 DASSQDCyDIPR Y406-p 0.585
## 5 PKP3 ADyDTLSLR Y176-p 0.544
## 6 ANXA1 GGPGSAVSPyPTFNPSSDVAALHK Y39-p 0.542
## 7 PEAK1 VPIVINPNAyDNLAIYK Y635-p 0.494
## 8 CTNND1 HYEDGYPGGSDNyGSLSR Y228-p 0.472
## 9 EPHA2 QSPEDVyFSK Y575-p 0.413
## 10 ITGB4 DySTLTSVSSHDSR Y1510-p 0.390
## # ℹ 56 more rowsRun_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 17 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 RET DVYEEDSyVK Y905-p 0.699
## 2 RET DVYEEDSyVKR Y905-p 0.699
## 3 PRKCD TGVAGEDMQDNSGTyGK Y334-p 0.361
## 4 STAT1 GTGyIKTELISVSEVHPSR Y701-p 0.196
## 5 CAV1 YVDSEGHLyTVPIR Y14-p 0.152
## 6 PTK2 YMEDSTyYK Y576-p 0.0995
## 7 GJA1 QASEQNWANySAEQNR Y313-p -0.134
## 8 PFN1 CyEMASHLR Y129-p -0.345
## 9 CTNNB1 NEGVATyAAAVLFR Y654-p -0.403
## 10 PRKCD RSDSASSEPVGIyQGFEK Y313-p -0.479
## 11 PRKCD RSDsASSEPVGIyQGFEK Y313-p -0.479
## 12 PRKCD RSDSASSEPVGIyQGFEKK Y313-p -0.479
## 13 PRKCD SDSASSEPVGIyQGFEK Y313-p -0.479
## 14 PTPRA VVQEYIDAFSDyANFK Y798-p -0.645
## 15 MPZL1 SESVVyADIR Y263-p -0.760
## 16 SHC1 ELFDDPSyVNVQNLDK Y427-p -1.73
## 17 ARHGAP35 NEEENIySVPHDSTQGK Y1105-p -1.83Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 5 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 EPHA2 QSPEDVyFSK Y575-p 0.413
## 2 EPHA2 VLEDDPEATyTTSGGK Y772-p 0.222
## 3 EPHA2 VLEDDPEATyTTSGGKIPIR Y772-p 0.222
## 4 CLDN4 SAAASNyV Y208-p 0.0958
## 5 EPHA2 TYVDPHTyEDPNQAVLK Y594-p 0.0727Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Ret/RET")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 18 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 RET VGPGyLGSGGSR Y826-p 1.50
## 2 RET DVYEEDSyVK Y905-p 0.699
## 3 RET DVYEEDSyVKR Y905-p 0.699
## 4 RET RRDyLDLAASTPSDSLIYDDGLSEEETPLVDCNNAPLPR Y1015-p 0.322
## 5 MAPK14 HTDDEMTGyVATR Y182-p 0.206
## 6 PTK2 YMEDSTyYK Y576-p 0.0995
## 7 RET LyGMSDPNWPGESPVPLTR Y1062-p 0.0736
## 8 RET ADGTNTGFPRyPNDSVYANWMLSPSAAK Y1090-p 0.0643
## 9 RET YPNDSVyANWMLSPSAAK Y1096-p -0.253
## 10 MAPK9 TACTNFMMTPyVVTR Y185-p -0.318
## 11 RET DVyEEDSYVK Y900-p -0.398
## 12 RET DVyEEDSYVKR Y900-p -0.398
## 13 MAPK1 VADPDHDHTGFLTEyVATR Y187-p -0.610
## 14 MAPK1 VADPDHDHTGFLtEyVATR Y187-p -0.610
## 15 MAPK3 IADPEHDHTGFLTEyVATR Y204-p -0.846
## 16 MAPK3 IADPEHDHTGFLtEyVATR Y204-p -0.846
## 17 AFAP1L2 SSSSDEEyIYMNK Y54-p -0.868
## 18 PLCG1 NPGFyVEANPMPTFK Y783-p -0.930EBC vs ctrl
data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 1 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)
## [1] "Signal Transduction"
## [2] "Cytokine Signaling in Immune system"
## [3] "NPAS4 regulates expression of target genes"
## [4] "RET signaling"
## [5] "RAF-independent MAPK1/3 activation"
## [6] "Fcgamma receptor (FCGR) dependent phagocytosis"
## [7] "Insulin receptor signalling cascade"
## [8] "Signaling by Erythropoietin"
## [9] "GPCR downstream signalling"
## [10] "FCERI mediated MAPK activation"PTM-SEA
GSEA_EBC_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PERT-PSP_ANTI_CD3"GSEA_EBC_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))## # A tibble: 1 × 8
## pathway pval padj log2err ES NES size leadingEdge
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list>
## 1 PERT-PSP_ANTI_CD3 0.00000791 0.00240 0.593 -0.875 -2.02 9 <chr [8]>Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PERT-PSP_ANTI_CD3"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 66 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 ANXA1 GGPGSAVSPyPTFNPSSDVAALHK Y39-p 0.101
## 2 NEDD9 TGHGYVyEYPSR Y166-p 0.0854
## 3 VCL SFLDSGyR Y822-p -0.0366
## 4 ACP1 QLIIEDPyYGNDSDFETVYQQCVR Y132-p -0.0908
## 5 PKP3 GQyHTLQAGFSSR Y84-p -0.103
## 6 EPHA2 TYVDPHTyEDPNQAVLK Y594-p -0.225
## 7 CDK1 IGEGTYGVVyKGR Y19-p -0.225
## 8 SLC38A2 SHyADVDPENQNFLLESNLGK Y41-p -0.257
## 9 SLC38A2 SHyADVDPENQNFLLESNLGKK Y41-p -0.257
## 10 EFNB2 TADSVFCPHyEK Y304-p -0.263
## # ℹ 56 more rowsRun_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PERT-PSP_ERLOTINIB" "PERT-PSP_ANTI_CD3"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 17 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 CAV1 YVDSEGHLyTVPIR Y14-p -0.311
## 2 PFN1 CyEMASHLR Y129-p -0.406
## 3 GJA1 QASEQNWANySAEQNR Y313-p -0.499
## 4 STAT1 GTGyIKTELISVSEVHPSR Y701-p -0.627
## 5 RET DVYEEDSyVK Y905-p -0.808
## 6 RET DVYEEDSyVKR Y905-p -0.808
## 7 PTK2 YMEDSTyYK Y576-p -0.866
## 8 MPZL1 SESVVyADIR Y263-p -0.910
## 9 PRKCD TGVAGEDMQDNSGTyGK Y334-p -1.17
## 10 CTNNB1 NEGVATyAAAVLFR Y654-p -1.33
## 11 PTPRA VVQEYIDAFSDyANFK Y798-p -1.42
## 12 PRKCD RSDSASSEPVGIyQGFEK Y313-p -1.52
## 13 PRKCD RSDsASSEPVGIyQGFEK Y313-p -1.52
## 14 PRKCD RSDSASSEPVGIyQGFEKK Y313-p -1.52
## 15 PRKCD SDSASSEPVGIyQGFEK Y313-p -1.52
## 16 ARHGAP35 NEEENIySVPHDSTQGK Y1105-p -2.65
## 17 SHC1 ELFDDPSyVNVQNLDK Y427-p -4.74Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PERT-PSP_ERLOTINIB" "PERT-PSP_ANTI_CD3"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 5 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 EPHA2 TYVDPHTyEDPNQAVLK Y594-p -0.225
## 2 CLDN4 SAAASNyV Y208-p -0.301
## 3 EPHA2 QSPEDVyFSK Y575-p -0.365
## 4 EPHA2 VLEDDPEATyTTSGGK Y772-p -0.790
## 5 EPHA2 VLEDDPEATyTTSGGKIPIR Y772-p -0.790EC vs E
data_diff_EC_vs_E_pY <- test_diff(pY_se_Set2, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, contrast = "EC_vs_E", add_names = TRUE, additional_title = "pY", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## [1] "Axon guidance"
## [2] "ERKs are inactivated"
## [3] "Generation of second messenger molecules"
## [4] "Intracellular signaling by second messengers"
## [5] "Fcgamma receptor (FCGR) dependent phagocytosis"
## [6] "Costimulation by the CD28 family"
## [7] "Signaling by Receptor Tyrosine Kinases"
## [8] "Signaling by FGFR1"
## [9] "Signaling by ERBB2"
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)PTM-SEA
GSEA_EC_vs_E_PTM <- Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PERT-PSP_ANTI_CD3"GSEA_EC_vs_E_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))## # A tibble: 1 × 8
## pathway pval padj log2err ES NES size leadingEdge
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list>
## 1 PERT-PSP_ANTI_CD3 0.000141 0.0426 0.519 -0.850 -1.84 9 <chr [7]>Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 66 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 CDK5 IGEGTyGTVFK Y15-p 0.434
## 2 ITSN2 LIyLVPEK Y553-p 0.402
## 3 PKP3 ADyDTLSLR Y176-p 0.378
## 4 PIK3R1 DQyLMWLTQK Y580-p 0.311
## 5 HIPK3 TVCSTyLQSR Y359-p 0.238
## 6 PRPF4B LCDFGSASHVADNDITPyLVSR Y849-p 0.235
## 7 DSP GVITDQNSDGyCQTGTMSR Y56-p 0.235
## 8 CDK1 IGEGTYGVVyKGR Y19-p 0.112
## 9 VCL SFLDSGyR Y822-p 0.0576
## 10 PKP3 GQyHTLQAGFSSR Y84-p 0.0496
## # ℹ 56 more rowsRun_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PERT-PSP_ANTI_CD3"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 17 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 PFN1 CyEMASHLR Y129-p -0.0134
## 2 RET DVYEEDSyVK Y905-p -0.0315
## 3 RET DVYEEDSyVKR Y905-p -0.0315
## 4 CTNNB1 NEGVATyAAAVLFR Y654-p -0.0798
## 5 STAT1 GTGyIKTELISVSEVHPSR Y701-p -0.103
## 6 GJA1 QASEQNWANySAEQNR Y313-p -0.135
## 7 PTK2 YMEDSTyYK Y576-p -0.229
## 8 MPZL1 SESVVyADIR Y263-p -0.309
## 9 CAV1 YVDSEGHLyTVPIR Y14-p -0.387
## 10 PTPRA VVQEYIDAFSDyANFK Y798-p -0.759
## 11 PRKCD RSDSASSEPVGIyQGFEK Y313-p -0.911
## 12 PRKCD RSDsASSEPVGIyQGFEK Y313-p -0.911
## 13 PRKCD RSDSASSEPVGIyQGFEKK Y313-p -0.911
## 14 PRKCD SDSASSEPVGIyQGFEK Y313-p -0.911
## 15 PRKCD TGVAGEDMQDNSGTyGK Y334-p -0.987
## 16 SHC1 ELFDDPSyVNVQNLDK Y427-p -1.09
## 17 ARHGAP35 NEEENIySVPHDSTQGK Y1105-p -1.62Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PERT-PSP_ANTI_CD3"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 5 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 CLDN4 SAAASNyV Y208-p -0.216
## 2 EPHA2 TYVDPHTyEDPNQAVLK Y594-p -0.310
## 3 EPHA2 QSPEDVyFSK Y575-p -0.875
## 4 EPHA2 VLEDDPEATyTTSGGK Y772-p -0.978
## 5 EPHA2 VLEDDPEATyTTSGGKIPIR Y772-p -0.978EBC vs EC
data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set2, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## character(0)## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)PTM-SEA
GSEA_EBC_vs_EC_PTM <- Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)GSEA_EBC_vs_EC_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))## # A tibble: 0 × 8
## # ℹ 8 variables: pathway <chr>, pval <dbl>, padj <dbl>, log2err <dbl>,
## # ES <dbl>, NES <dbl>, size <int>, leadingEdge <list>Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 66 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 CDK1 IGEGTYGVVyKGR Y19-p 0.207
## 2 VCL SFLDSGyR Y822-p 0.122
## 3 GRB2 NyVTPVNR Y209-p 0.101
## 4 DYRK3 LYTyIQSR Y369-p -0.0298
## 5 PFN1 CyEMASHLR Y129-p -0.0607
## 6 ACP1 QLIIEDPyYGNDSDFETVYQQCVR Y132-p -0.116
## 7 MPZL1 SESVVyADIR Y263-p -0.150
## 8 EFNB2 TADSVFCPHyEK Y304-p -0.162
## 9 FRK HGHyFVALFDYQAR Y46-p -0.187
## 10 DSG2 VYAPASTLVDQPyANEGTVVVTER Y979-p -0.254
## # ℹ 56 more rowsRun_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 17 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 PFN1 CyEMASHLR Y129-p -0.0607
## 2 MPZL1 SESVVyADIR Y263-p -0.150
## 3 GJA1 QASEQNWANySAEQNR Y313-p -0.364
## 4 CAV1 YVDSEGHLyTVPIR Y14-p -0.463
## 5 PTPRA VVQEYIDAFSDyANFK Y798-p -0.773
## 6 ARHGAP35 NEEENIySVPHDSTQGK Y1105-p -0.818
## 7 STAT1 GTGyIKTELISVSEVHPSR Y701-p -0.823
## 8 CTNNB1 NEGVATyAAAVLFR Y654-p -0.926
## 9 PTK2 YMEDSTyYK Y576-p -0.966
## 10 PRKCD RSDSASSEPVGIyQGFEK Y313-p -1.05
## 11 PRKCD RSDsASSEPVGIyQGFEK Y313-p -1.05
## 12 PRKCD RSDSASSEPVGIyQGFEKK Y313-p -1.05
## 13 PRKCD SDSASSEPVGIyQGFEK Y313-p -1.05
## 14 RET DVYEEDSyVK Y905-p -1.51
## 15 RET DVYEEDSyVKR Y905-p -1.51
## 16 PRKCD TGVAGEDMQDNSGTyGK Y334-p -1.53
## 17 SHC1 ELFDDPSyVNVQNLDK Y427-p -3.01Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 5 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 EPHA2 TYVDPHTyEDPNQAVLK Y594-p -0.297
## 2 CLDN4 SAAASNyV Y208-p -0.397
## 3 EPHA2 QSPEDVyFSK Y575-p -0.778
## 4 EPHA2 VLEDDPEATyTTSGGK Y772-p -1.01
## 5 EPHA2 VLEDDPEATyTTSGGKIPIR Y772-p -1.01Serine/threonine
Each condition vs ctrl
data_diff_E_vs_ctrl_pST <- test_diff(pST_se_Set2, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_E_vs_ctrl_pST <- add_rejections_SH(data_diff_E_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_E_vs_ctrl_pST, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set2_form, dep_E_vs_ctrl_pST, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## character(0)
## Note: Row-scaling applied for this heatmap
data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set2, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## character(0)
## Note: Row-scaling applied for this heatmap
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set2, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some of the pathways the P-values were likely overestimated. For
## such pathways log2err is set to NA.## [1] "Signal Transduction" "Innate Immune System"
## [3] "Formation of the cornified envelope"
## Note: Row-scaling applied for this heatmap
EC vs E
data_diff_EC_vs_E_pST <- test_diff(pST_se_Set2, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, additional_title = "pST", contrast = "EC_vs_E", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## [1] "CDC42 GTPase cycle" "Cell junction organization"
## [3] "Formation of the cornified envelope"#data_results <- get_df_long(dep)EBC vs EC
data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set2, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## [1] "PTK6 Regulates Proteins Involved in RNA Processing"
## [2] "Signal Transduction"
## [3] "RHOG GTPase cycle"
## [4] "CDC42 GTPase cycle"#data_results <- get_df_long(dep)Save
rowData(dep_E_vs_ctrl_pY) %>% as_tibble() %>% select(HGNC_Symbol, E_vs_ctrl_diff) %>% write.table("../Data/Kinase_enrichment/Batch1_Set2_E_vs_ctrl_pY_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")
rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>% select(HGNC_Symbol, EC_vs_ctrl_diff) %>% write.table("../Data/Kinase_enrichment/Batch1_Set2_EC_vs_ctrl_pY_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")
rowData(dep_E_vs_ctrl_pY) %>% as_tibble() %>%
select(HGNC_Symbol, ends_with("_diff")) %>%
group_by(HGNC_Symbol) %>%
mutate(abs_FC = abs(E_vs_ctrl_diff) ) %>%
arrange(desc( abs_FC) ) %>%
slice(1) %>%
ungroup() %>%
select(HGNC_Symbol, ends_with("_diff") ) %>%
write.table("../Data/Kinase_enrichment/Batch1_Set2_E_vs_ctrl_pY_mostextremeFCperprotein_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")
rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>%
select(HGNC_Symbol, ends_with("_diff")) %>%
group_by(HGNC_Symbol) %>%
mutate(abs_FC = abs(EC_vs_ctrl_diff) ) %>%
arrange(desc( abs_FC) ) %>%
slice(1) %>%
ungroup() %>%
select(HGNC_Symbol, ends_with("_diff") ) %>%
write.table("../Data/Kinase_enrichment/Batch1_Set2_ECs_vs_ctrl_pY_mostextremeFCperprotein_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")
rowData(dep_E_vs_ctrl_pY) %>% as_tibble() %>%
filter(E_vs_ctrl_diff>1) %>%
select(HGNC_Symbol ) %>% unique() %>%
write.table("../Data/Kinase_enrichment/Batch1_Set2_E_vs_ctrl_pY_FCmorethan1_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")
rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>%
filter(EC_vs_ctrl_diff>1) %>%
select(HGNC_Symbol ) %>% unique() %>%
write.table("../Data/Kinase_enrichment/Batch1_Set2_EC_vs_ctrl_pY_FCmorethan1_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")Session Info
sessionInfo()## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] lubridate_1.9.2 forcats_1.0.0
## [3] stringr_1.5.0 dplyr_1.1.2
## [5] purrr_1.0.2 readr_2.1.4
## [7] tidyr_1.3.0 tibble_3.2.1
## [9] ggplot2_3.4.2 tidyverse_2.0.0
## [11] mdatools_0.14.0 SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
## [15] MatrixGenerics_1.10.0 matrixStats_1.0.0
## [17] DEP_1.20.0 org.Hs.eg.db_3.16.0
## [19] AnnotationDbi_1.60.2 IRanges_2.32.0
## [21] S4Vectors_0.36.2 Biobase_2.58.0
## [23] BiocGenerics_0.44.0 fgsea_1.24.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.4.3 circlize_0.4.15 fastmatch_1.1-4
## [4] plyr_1.8.8 igraph_1.5.1 gmm_1.8
## [7] lazyeval_0.2.2 shinydashboard_0.7.2 crosstalk_1.2.0
## [10] BiocParallel_1.32.6 digest_0.6.33 foreach_1.5.2
## [13] htmltools_0.5.6 fansi_1.0.4 magrittr_2.0.3
## [16] memoise_2.0.1 cluster_2.1.4 doParallel_1.0.17
## [19] tzdb_0.4.0 limma_3.54.2 ComplexHeatmap_2.14.0
## [22] Biostrings_2.66.0 imputeLCMD_2.1 sandwich_3.0-2
## [25] timechange_0.2.0 colorspace_2.1-0 blob_1.2.4
## [28] xfun_0.40 crayon_1.5.2 RCurl_1.98-1.12
## [31] jsonlite_1.8.7 impute_1.72.3 zoo_1.8-12
## [34] iterators_1.0.14 glue_1.6.2 hash_2.2.6.2
## [37] gtable_0.3.3 zlibbioc_1.44.0 XVector_0.38.0
## [40] GetoptLong_1.0.5 DelayedArray_0.24.0 shape_1.4.6
## [43] scales_1.2.1 pheatmap_1.0.12 vsn_3.66.0
## [46] mvtnorm_1.2-2 DBI_1.1.3 Rcpp_1.0.11
## [49] plotrix_3.8-2 mzR_2.32.0 viridisLite_0.4.2
## [52] xtable_1.8-4 clue_0.3-64 reactome.db_1.82.0
## [55] bit_4.0.5 preprocessCore_1.60.2 sqldf_0.4-11
## [58] MsCoreUtils_1.10.0 DT_0.28 htmlwidgets_1.6.2
## [61] httr_1.4.6 gplots_3.1.3 RColorBrewer_1.1-3
## [64] ellipsis_0.3.2 farver_2.1.1 pkgconfig_2.0.3
## [67] XML_3.99-0.14 sass_0.4.7 utf8_1.2.3
## [70] STRINGdb_2.10.1 labeling_0.4.2 tidyselect_1.2.0
## [73] rlang_1.1.1 later_1.3.1 cellranger_1.1.0
## [76] munsell_0.5.0 tools_4.2.3 cachem_1.0.8
## [79] cli_3.6.1 gsubfn_0.7 generics_0.1.3
## [82] RSQLite_2.3.1 fdrtool_1.2.17 evaluate_0.21
## [85] fastmap_1.1.1 mzID_1.36.0 yaml_2.3.7
## [88] knitr_1.43 bit64_4.0.5 caTools_1.18.2
## [91] KEGGREST_1.38.0 ncdf4_1.21 mime_0.12
## [94] compiler_4.2.3 rstudioapi_0.15.0 plotly_4.10.2
## [97] png_0.1-8 affyio_1.68.0 stringi_1.7.12
## [100] bslib_0.5.0 highr_0.10 MSnbase_2.24.2
## [103] lattice_0.21-8 ProtGenerics_1.30.0 Matrix_1.6-0
## [106] tmvtnorm_1.5 vctrs_0.6.3 pillar_1.9.0
## [109] norm_1.0-11.1 lifecycle_1.0.3 BiocManager_1.30.22
## [112] jquerylib_0.1.4 MALDIquant_1.22.1 GlobalOptions_0.1.2
## [115] data.table_1.14.8 cowplot_1.1.1 bitops_1.0-7
## [118] httpuv_1.6.11 R6_2.5.1 pcaMethods_1.90.0
## [121] affy_1.76.0 promises_1.2.1 KernSmooth_2.23-22
## [124] codetools_0.2-19 MASS_7.3-60 gtools_3.9.4
## [127] assertthat_0.2.1 chron_2.3-61 proto_1.0.0
## [130] rjson_0.2.21 withr_2.5.0 GenomeInfoDbData_1.2.9
## [133] parallel_4.2.3 hms_1.1.3 grid_4.2.3
## [136] rmarkdown_2.23 shiny_1.7.4.1knitr::knit_exit()